Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, canonical correlation, etc. It is important to assess multivariate normality in order to proceed with such statistical methods. There are many analytical methods proposed for checking multivariate normality. However, deciding which method to use is a challenging process, since each method may give different results under certain conditions. Hence, we may say that there is no best method, which is valid under any condition, for normality checking. In addition to numerical results, it is very useful to use graphical methods to decide on multivariate normality. Combining the numerical results from several methods with graphical approaches can be useful and provide more reliable decisions.

Here, we present a web-tool application to assess multivariate normality. This application uses the MVN package from R. This tool contains the three most widely used multivariate normality tests, including Mardia’s, Henze-Zirkler’s and Royston’s, and graphical approaches, including chi-square Q-Q, perspective and contour plots (Multivariate analysis tab). It also includes two multivariate outlier detection methods, which are based on robust Mahalanobis distances (Outlier detection tab). Moreover, this web-tool performs the univariate normality of marginal distributions through both tests and plots (Univariate analysis tab). More detailed information about the tests, graphical approaches and their implementations through this web-tool and MVN package can be found in the paper of the package. All source codes are in GitHub.

If you use this tool for your research please cite: Korkmaz S, Goksuluk D, Zararsiz G. MVN: An R Package for Assessing Multivariate Normality. The R Journal. 2014 6(2):151-162.

Usage of the web-tool

In order to use this application,

(i) load your data set using Data upload tab. If data set has a group variable, users can define whether this variable is in the first or last column then the analysis will be performed in each sub-group,

(ii) check univariate normality through univariate normality tests and plots in the Univariate analysis tab. Users also can get descriptive statistics using this tab,